Dual thrombolytic therapy with mutant pro-urokinase and small bolus alteplase for ischemic stroke (DUMAS): study protocol for a multicenter randomized controlled phase II trial

Background The effectiveness of alteplase for ischemic stroke treatment is limited, partly due to the occurrence of intracranial and extracranial hemorrhage. Mutant pro-urokinase (m-proUK) does not deplete fibrinogen and lyses fibrin only after induction with alteplase. Therefore, this treatment has the potential to be safer and more efficacious than treatment with alteplase alone. The aim of this study is to assess the safety and efficacy of thrombolytic treatment consisting of a small bolus alteplase followed by m-proUK compared with standard thrombolytic treatment with alteplase in patients presenting with ischemic stroke. Methods DUMAS is a multicenter, phase II trial with a prospective randomized open-label blinded end-point (PROBE) design, and an adaptive design for dose optimization. Patients with ischemic stroke, who meet the criteria for treatment with intravenous (IV) alteplase can be included. Patients eligible for endovascular thrombectomy are excluded. Patients are randomly assigned (1:1) to receive a bolus of IV alteplase (5mg) followed by a continuous IV infusion of m-proUK (40 mg/h during 60 min) or usual care with alteplase (0.9 mg/kg). Depending on the results of interim analyses, the dose of m-proUK may be revised to a lower dose (30 mg/h during 60 min) or a higher dose (50 mg/h during 60 min). We aim to include 200 patients with a final diagnosis of ischemic stroke. The primary outcome is any post-intervention intracranial hemorrhage (ICH) on neuroimaging at 24 h according to the Heidelberg Bleeding Classification, analyzed with binary logistic regression. Efficacy outcomes include stroke severity measured with the National Institutes of Health Stroke Scale (NIHSS) at 24 h and 5–7 days, score on the modified Rankin scale (mRS) assessed at 30 days, change (pre-treatment vs. post-treatment) in abnormal perfusion volume, and blood biomarkers of thrombolysis at 24 h. Secondary safety endpoints include symptomatic intracranial hemorrhage, death, and major extracranial hemorrhage. This trial will use a deferred consent procedure. Discussion When dual thrombolytic therapy with a small bolus alteplase and m-proUK shows the anticipated effect on the outcome, this will lead to a 13% absolute reduction in the occurrence of ICH in patients with ischemic stroke. Trial registration NL7409 (November 26, 2018)/NCT04256473 (February 5, 2020) Supplementary Information The online version contains supplementary material available at 10.1186/s13063-022-06596-z.


Introduction
Background This document describes the features of the simulated design, including the statistical models, decision rules, and simulation scenarios as input into the FACTS (Fixed and Adaptive Clinical Trial Simulator) software. A small set of operating characteristics for the simulations is also summarized. The goal of this design is to provide a set of prospectively defined, quantitative decision rules to guide interim analyses in the DUMAS trial. In this design, the DUMAS trial can either proceed to the maximum sample size without any changes, or it can transition to a lower or higher dose of the investigational drug at an interim analysis.

Endpoints
The primary endpoint is freedom from any intracranial hemorrhage (NoICH) after stroke thrombolysis (dichotomous) and is measured within 24 (to 48) hours. The secondary endpoint is clinical improvement within 24 hours (Clin), also dichotomous. A positive outcome is indicated by a value of 1, and a negative outcome (presence of ICH or failure to clinically improve) is indicated by a value of 0. Clinical improvement is defined as improvement of at least 4 points on the National Institute of Health Stroke Scale (NIHSS) at 24 hours compared to baseline, or (near) complete recovery (NIHSS 0 or 1).

Treatment Arms
3 The trial will enroll up to a maximum of 200 subjects with a discharge diagnosis of ischemic stroke, randomized among 2 arms, including a control arm. We have 1 treatment arm which we label generically by their arm index as: d= 0 (controlstandard alteplase dosing), 1 (treatmentinvestigational thrombolytic regimenalso known as mutant pro-urokinase (mproUK; HisproUK).

Statistical modeling
This section describes the statistical modeling used in the design. The modeling is Bayesian in nature.

Final Endpoint Model
The following models are fit separately for the primary and secondary endpoint.
Let be the primary outcome measured at 24 hours for the ℎ subject. We model the outcomes is the underlying response rate for arm . We transform the response rates onto the 1 ∼ (0, 2 2 ).
Thus, for each dose is estimated separately using only data from that dose.

Evaluation of Posterior Estimates
Posterior estimates are independently calculated for each endpoint.
The Bayesian final endpoint model is fitted to the data at each update. The posterior is calculated as: where is the set of parameters for the final endpoint model, ( ) is the prior for those parameters, is the final response for each subject, and is the number of subjects. The posterior is evaluated using MCMC with individual parameters updated by Metropolis Hastings (or Gibbs sampling where possible), using only the data available at the time of the update.

Quantities of Interest
We define a number of quantities that will be tracked and may be used to make decisions during the trial.

Posterior Probabilities
For each dose, we calculate the following quantities from the posterior:  For the primary endpoint (NoICH), the probability that the mean response on dose is greater than on control by at least 0.05:  For the secondary endpoint (Clin), the probability that the mean response on dose is greater than on control by at least 0.1:

Conventions for Missing Data
At any analysis, some subjects may have missing data for the final endpoint. The missing data could result from the subject dropping out of the study, or because the subject simply has not yet reached the final visit.
If the subject has not yet reached the final visit, the endpoint value is imputed from the estimate of the response for the subjects treatment arm (effectively contributing no information to the update of that estimate).
For any subject whose final endpoint is unknown due to drop out, the final outcome will be multiply imputed from the Bayesian model.

Study Design
Timing of Interim Analyses for dose adaptation The first interim will occur after 60 subjects with a discharge diagnosis of ischemic stroke have data up to 48 hours. Subsequent interims will be conducted after inclusion of every 20 patients with a discharge diagnosis of ischemic stroke and will continue until full accrual. Since interims are defined by calendar time, the total number of planned interims, , is random and will depend on the rate at which subjects accrue to the trial. Note that in the initial phase of the trial, mixed quantitative-qualitative review for safety will be carried out by the DSMB, after inclusion of every 10 patients.

Allocation
The trial will enroll 200 subjects with a discharge diagnosis of ischemic stroke that will be randomized to the treatment arms in a fixed ratio. Randomization will occur in blocks of variable sizes.

Changing to a higher dose
For interim 1-, the trial may transition to a higher dose if all of the following criteria are satisfied: Note that, as per protocol, the results of the interim analysis will be presented to the DSMB, who will advise the chair of the Steering Committee.

Final Evaluation Criteria
At the final analysis, the trial will be considered successful based on the primary endpoint analysis defined in the statistical analysis plan and in the main clinical protocol.

Simulation Scenarios
We evaluate the proposed design through trial simulation. We hypothesize several possible underlying truths for the mean response, as well as for trial execution variables such as accrual and dropout. For each of these scenarios, we generate data according to those truths and run through the design as specified above. We repeat this process to create multiple "virtual trials" and we track the behavior of each trial. In this section, we describe the parameters used to generate the virtual subject-level data. Simulations provided below provide what happens until either the trial reaches the maximum sample size without triggering a dose adjustment OR whether a dose change rule is triggered. For example, if a dose increase is recommended at 120 patients, the last 80 patients would be randomized 1:1 to the new dose versus control.

Virtual Subject Response Profiles
We consider 7 profiles for which subject outcomes for the final endpoints are simulated to have response rates as shown in Table 1. We assume two patients per week for just under 2 years. We simulate the random arrival of subjects into the trial from a Poisson process with the mean weekly rates specified in Table 2.
Within each accrual profile, there may be differential recruitment rates over time and across regions. Currently, we simulated only one region for recruitment. Thus, for each region, we specify:  the mean number of subjects per week at peak accrual,  the start date (in weeks from the start of the trial),  whether the region will have a ramp up phase, and if so, when the ramp up will be complete, and  whether the region will have a ramp down phase, and if so, when the ramp down will begin and when it will be complete. Ramp up and ramp down define simple linear increases and decreases in the mean recruitment rate from the start to the end of the ramp.
Thus some simulated trials recruit more quickly than this and some more slowly. We assume no dropouts for the purpose of this simulation.

Operating Characteristics
For the scenarios described above, we simulate multiple virtual trials and track the behavior of each trial, including the preliminary or final outcome of the trial, the estimated mean response, etc. In this study, the trial will continue with a new dose replacing the initial dose in the event a decision rule is triggered. The results in this section are summarized across all simulated trials for each scenario.

Overall
This section gives a high-level description of the operating characteristics. Table 3   This section summarizes the outcomes of the simulated trials. For each scenario in Table 4, the columns represent the proportion of simulated trials meeting each of the following definitions:  Participant timeline #13 Time schedule of enrolment, interventions (including any run-ins and washouts), assessments, and visits for participants. A schematic diagram is highly recommended (see Figure) P 9: L 1-18, P 31: L 1-9 Sample size #14 Estimated number of participants needed to achieve study objectives and how it was determined, including clinical and statistical assumptions supporting any sample size calculations P 14: L 8-21, P 15: L 1-2 Recruitment #15 Strategies for achieving adequate participant enrolment to reach target sample size P 6: L 20-21

Methods: Assignment of interventions (for controlled trials)
Allocation: sequence generation #16a Method of generating the allocation sequence (eg, computer-generated random numbers), and list of any factors for stratification. To reduce predictability of a random sequence, details of any planned restriction (eg, blocking) should be provided in a separate document that is unavailable to those who enrol participants or assign interventions P 7: L 18-22 Allocation concealment mechanism #16b Mechanism of implementing the allocation sequence (eg, central telephone; sequentially numbered, opaque, sealed envelopes), describing any steps to conceal the sequence until interventions are assigned P 7: L 18-22 Allocation: implementation #16c Who will generate the allocation sequence, who will enrol participants, and who will assign participants to interventions P 7: L 18-22, P 8: L 1-8 Blinding (masking) #17a Who will be blinded after assignment to interventions (eg, trial participants, care providers, outcome assessors, data analysts), and how #21b Description of any interim analyses and stopping guidelines, including who will have access to these interim results and make the final decision to terminate the trial P 11: L 3-22, P 12: L 1-6 Harms #22 Plans for collecting, assessing, reporting, and managing solicited and spontaneously reported adverse events and other unintended effects of trial interventions or trial conduct P 11: L 3-22, P 12: L 1-6 Auditing #23 Frequency and procedures for auditing trial conduct, if any, and whether the process will be independent from investigators and the sponsor P 12: L 8-9

Ethics and dissemination
Research ethics approval #24 Plans for seeking research ethics committee / institutional review board (REC / IRB) approval P 15: L 12-19 Protocol amendments #25 Plans for communicating important protocol modifications (eg, changes to eligibility criteria, outcomes, analyses) to relevant parties (eg, investigators, REC / IRBs, trial participants, trial registries, journals, regulators) P 15: L 12-19 Consent or assent #26a Who will obtain informed consent or assent from potential trial participants or authorised surrogates, and how (see Item 32) P 9: L 19-22, P 10: L 1-13 Consent or assent: ancillary studies #26b Additional consent provisions for collection and use of participant data and biological specimens in ancillary studies, if applicable P 9: L 19-22, P 10: L 1-13, P 19: L 19-20, P 20: L 1-2 Confidentiality #27 How personal information about potential and enrolled participants will be collected, shared, and maintained in order to protect confidentiality before, during, and after the trial P 12: L 9-13 Declaration of interests #28 Financial and other competing interests for principal investigators for the overall trial and each study site P 20: L 11-19 Data access #29 Statement of who will have access to the final trial dataset, and disclosure of contractual agreements that limit such access for investigators